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Matching ranges over many dimensions

asked 2013-04-08 03:03:56 -0500

JamesHammond gravatar image

updated 2013-04-08 10:33:33 -0500

Visualize many lanes of binary valued time ranges. Are there any existing methods for finding pairs or lanes that feature many overlapping regions?

There are hundreds to thousands of these lanes. The are not ordered, so the matching dimensions are not necessarily neighboring pixels.

I am both interested in lanes that have many co-occurrences of start points '=' and overlaps.

|----=-=-=-=-====--------======----====---=|
|===--=-=-=-=-=-=-------=---=-===--=----===-|
|----=--------=======---======---========-|
|=====--=--=-------------=----=======---====|

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answered 2013-04-08 03:37:28 -0500

Basically you want a descriptor matcher, that uses Hamming distance to calculate equality between two sets of descriptors.

The following link shows the available information and functionality in openCV:

http://docs.opencv.org/modules/features2d/doc/common_interfaces_of_descriptor_matchers.html?highlight=matcher#BFMatcher%20:%20public%20DescriptorMatcher

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Hmm, for binary images features2D are probably not the best choice but maybe he has his lanes also in grayscale, then this will work nicely. @JamesHammond: All in all your description of your problem is too vague, formulate your question more comprehensively! And before that - have a look at the docs and this Q&A forum!

Guanta gravatar imageGuanta ( 2013-04-08 04:46:51 -0500 )edit
1

Thats just it, you can assign a single matcher to an own created descriptor vector. Its just bits and bytes. Matcher only uses a distance metric.

StevenPuttemans gravatar imageStevenPuttemans ( 2013-04-08 04:56:05 -0500 )edit

Definitly!

Guanta gravatar imageGuanta ( 2013-04-08 05:16:39 -0500 )edit

Hi, I've added a bit of detail to my question. Given the depiction, is 'assign a single matcher to an own created descriptor vector' the way to go?

JamesHammond gravatar imageJamesHammond ( 2013-04-08 10:11:00 -0500 )edit
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Basically make a 0 - 1 string of your lanes. Then use a matcher to check the hamming distance between both. This link explains hamming distance a bit more, which is basically an equality measure for input data : http://www.princeton.edu/~achaney/tmve/wiki100k/docs/Hamming_distance.html

StevenPuttemans gravatar imageStevenPuttemans ( 2013-04-08 12:14:25 -0500 )edit
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Asked: 2013-04-08 03:03:56 -0500

Seen: 88 times

Last updated: Apr 08 '13